This study will use 1985-89 personnel data and medical care claims from a large employer to develop a method for measuring and compensating for adverse selection among competing health insurance plans. A six equation maximum likelihood model for predicting medical care utilization and expenditures will be estimated using the claims data as dependent variables and the personnel data as independent variables. Diagnostic information from the claims data will be used to identify particularly high risk individuals most likely to be the objects of risk selection strategies; a variety of special techniques will be developed to risk-adjust employer contributions for these individuals. Two sets of model parameters will be estimated, one based on annual allowed charges incurred by enrollees in the employer's fee-for-service (FFS) plan and one based on annual expenditures for enrollees in a prepaid group practice Health Maintenance Organization (HMO) with a large market share. Application of the FFS parameters to the HMO enrollees and of the HMO parameters to the FFS plan enrollees will generate two alternative measures of biased selection based on the benefit packages and styles of practice characteristic of the two alternative plans. Predicted expenditures will also be generated for enrollees in the several smaller HMOs offered by the employer to measure adverse selection among competing HMOs. We will analyze predicted and actual expenditures for individuals switching from the FFS plan to an HMO or from one HMO to another and compare these to predicted and actual expenditures for individuals continuously enrolled in particular plans.